Artificial Intelligence in Medical Education: Curriculum Design, Assessment Models, and Educational Infrastructure...

Artificial intelligence (AI) is rapidly becoming an integral part of everyday clinical practice, including cardiology and cardiovascular surgery. As AI increasingly influences diagnostic and therapeutic decisions, physicians are expected to interact with these systems in a critical, safe, and ethically grounded manner. This narrative review aims to explore how AI can be systematically integrated into undergraduate and residency medical education, with a particular focus on curriculum design, teaching strategies, assessment models, and educational infrastructure, while considering the context of the Turkish medical education system. A narrative synthesis of international medical education literature, policy documents, and institutional reports was conducted without quantitative meta-analysis. The review was guided by the principles of human-in-the-loop clinical reasoning, ethical AI use, and patient safety. Effective integration of AI into medical education requires a longitudinal and staged curriculum spanning preclinical, clinical, and residency training. Assessment strategies must explicitly address AI-assisted decision-making and be supported by transparent institutional policies governing AI use in examinations, as well as by secure, regulation-compliant digital infrastructure. Educational approaches should encourage learners to critically appraise and contextualize AI outputs rather than accept them uncritically. The reviewed literature supports a competency-based educational framework that integrates AI literacy, ethical reasoning, and context-aware clinical judgment. AI education should be viewed as a core clinical competency that strengthens rather than replaces human judgment. Particularly in high-risk cardiovascular disciplines, a standardized, ethics-centered, and competency-based educational framework is essential to prepare future physicians for AI-augmented healthcare environments.